论文标题
对光度星系调查的两点角度统计的快速仿真
Fast emulation of two-point angular statistics for photometric galaxy surveys
论文作者
论文摘要
我们开发了一组基于机器的宇宙学模拟器，以获得$ c（\ ell）$角功率谱系数的快速模型预测，这些系数表征了星系聚类和弱重力镜头的层析成像观察，并从多频段光度测量（及其交叉相关）中获得了弱重力镜头。一组神经网络经过训练，可以将宇宙学参数映射到系数中，从而在计算给定的宇宙学参数的所需统计数据时，就可以在标准的Boltzmann solvers中计算所需的统计数据，从而获得了加速$ \ Mathcal {O}（10^3）$，而准确的boltzmann solvers的准确性比0.175 $ $ $ $ $ $ <％$ <％$ <％$ <％$ <％。这对应于$ \ sim 2 \％$或更少的统计误差条中预期的IV级光度测量。通过（$ \ textIt {i} $）在培训阶段之前（$ \ textit {i} $）获得了速度和准确性的总体提高，并且（$ \ textit {ii} $）与先前的实现相比，是更有效的神经网络体系结构。
We develop a set of machine-learning based cosmological emulators, to obtain fast model predictions for the $C(\ell)$ angular power spectrum coefficients characterising tomographic observations of galaxy clustering and weak gravitational lensing from multi-band photometric surveys (and their cross-correlation). A set of neural networks are trained to map cosmological parameters into the coefficients, achieving a speed-up $\mathcal{O}(10^3)$ in computing the required statistics for a given set of cosmological parameters, with respect to standard Boltzmann solvers, with an accuracy better than $0.175\%$ ($<0.1\%$ for the weak lensing case). This corresponds to $\sim 2\%$ or less of the statistical error bars expected from a typical Stage IV photometric surveys. Such overall improvement in speed and accuracy is obtained through ($\textit{i}$) a specific pre-processing optimisation, ahead of the training phase, and ($\textit{ii}$) a more effective neural network architecture, compared to previous implementations.
